Introduction: To help monitor and manage complications, monitor disease progression, and to help lower morbidity and mortality rates in Hematopoietic cell transplant (HCT) patients, the use of artificial intelligence technology can prove to be an efficient tool. Aim: We propose a fureristic vision of an artificial intelligence model which could help in early detection of MSK related complications, improve communication between HCT healthcare professional team, improve diagnostics via machine learning (ML), help monitor symptom/disease progression remotely, and help integrate services for a more patient-friendly service delivery i.e. drug prescription, exercise prescription, appointment tracking, referral pathways. Materials and Methods: The proposed model is a three phase integrated program where musculoskeletal physical examination is combined with wearable textiles interface platform and machine learning algorithms thereby providing live and remote feedback of changes as they happen in at the musculoskeletal and vital signs level. Result: With the help of machine learning technology various algorithms can be created to help improve remote and live diagnostic accuracy of post-HCT musculoskeletal manifestations. Subtle changes over a course of time in various patient groups can be detected at the skin, fascia, muscle, bone level; thereby helping in better understanding of the disease and its management. Conclusion: A futuristic machine learning artificial intelligence program combined with wearable devices and the expertise of the clinicians can significantly change the way healthcare professionals and patients manage post-HCT complications and the resultant being improved quality of life in survivors of HCT.